Codes
Tick Chart for MetaTrader 4
The presented indicator plots a fully-functional tick chart similar to the standard price charts, with the ability of the analysis using all the MetaTrader features
Articles
Neural Networks in Trading: LSTM Optimization for Multivariate Time Series Forecasting (DA-CG-LSTM) for MetaTrader 5
This article introduces the DA-CG-LSTM algorithm, which offers new approaches to time series analysis and forecasting. It explains how innovative attention mechanisms and model flexibility can improve forecast accuracy
Neural Networks in Trading: Actor—Director—Critic (Final Part) for MetaTrader 5
The Actor–Director–Critic framework is an evolution of the classic agent learning architecture. The article presents practical experience of its implementation and adaptation to financial market conditions
Neural Networks in Trading: Actor—Director—Critic for MetaTrader 5
We invite you to explore the Actor-Director-Critic framework, which combines hierarchical learning and a multi-component architecture for creating adaptive trading strategies. In this article, we take a detailed look at how using the Director to classify the Actor's actions helps to effectively
Neural Networks in Trading: Skill Hierarchy for Adaptive Agent Behavior (Final Part) for MetaTrader 5
The article discusses the practical implementation of the HiSSD framework in algorithmic trading tasks. It explains how the skill hierarchy and adaptive architecture can be used to build sustainable trading strategies
Neural Networks in Trading: Hierarchical Skill Discovery for Adaptive Agent Behavior (HiSSD) for MetaTrader 5
In this article, we explore the HiSSD framework, which combines hierarchical learning and multi-agent approaches to create adaptive systems. We examine in detail how this innovative methodology helps uncover hidden patterns in financial markets and optimize trading strategies in decentralized
Neural Networks in Trading: Anomaly Detection in the Frequency Domain (Final Part) for MetaTrader 5
We continue to work on implementing the CATCH framework, which combines the Fourier transform and frequency patching mechanisms, ensuring accurate detection of market anomalies. In this article, we complete the implementation of our own vision of the proposed approaches and test the new models on
Neural Networks in Trading: Detecting Anomalies in the Frequency Domain (CATCH) for MetaTrader 5
The CATCH framework combines Fourier transform and frequency patching to accurately identify market anomalies beyond the reach of traditional methods. Let us examine how this approach reveals hidden patterns in financial data
Neural Networks in Trading: Adaptive Detection of Market Anomalies (Final Part) for MetaTrader 5
We continue to build the algorithms that form the basis of the DADA framework, which is an advanced tool for detecting anomalies in time series. This approach enables effective distinguishing random fluctuations from significant deviations. Unlike classical methods, DADA dynamically adapts to
Neural Networks in Trading: Adaptive Detection of Market Anomalies (DADA) for MetaTrader 5
We invite you to get acquainted with the DADA framework, which is an innovative method for detecting anomalies in time series. It helps distinguish random fluctuations from suspicious deviations. Unlike traditional methods, DADA is flexible and adapts to different data. Instead of a fixed
Neural Networks in Trading: Dual Clustering of Multivariate Time Series (Final Part) for MetaTrader 5
We continue to implement approaches proposed vy the authors of the DUET framework, which offers an innovative approach to time series analysis, combining temporal and channel clustering to uncover hidden patterns in the analyzed data